Lung cancer risk prediction models based on pulmonary nodules: A systematic review

• Published in: Thoracic cancer Vol. 13; no. 5; pp. 664 - 677
• Main Authors: Wu, Zheng, Wang, Fei, Cao, Wei, Qin, Chao, Dong, Xuesi, Yang, Zhuoyu, Zheng, Yadi, Luo, Zilin, Zhao, Liang, Yu, Yiwen, Xu, Yongjie, Li, Jiang, Tang, Wei, Shen, Sipeng, Wu, Ning, Tan, Fengwei, Li, Ni, He, Jie
• Format: Journal Article
• Language: English
• Published: Melbourne John Wiley & Sons Australia, Ltd 01.03.2022; John Wiley & Sons, Inc; Wiley
• Subjects: Algorithms; Bias; Deep learning; Disease; early detection and early diagnosis; Early Detection of Cancer - methods; Humans; Lung - pathology; Lung cancer; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - epidemiology; Medical prognosis; Medical screening; Methods; Mortality; Multiple Pulmonary Nodules - pathology; prediction; pulmonary nodule; Retrospective Studies; Review; screening; Variables; lung cancer; prediction; screening; pulmonary nodule; early detection and early diagnosis
• ISSN: 1759-7706; 1759-7714
• DOI: 10.1111/1759-7714.14333

Background Screening with low‐dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false‐positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models. Methods The keywords “lung cancer,” “lung neoplasms,” “lung tumor,” “risk,” “lung carcinoma” “risk,” “predict,” “assessment,” and “nodule” were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed. Results A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single‐center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets. Conclusion The existing models showed good discrimination for identifying high‐risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population. Pulmonary nodules risk prediction models were developed to reduce the high false‐positive rate of lung cancer screening. A total of 41 articles and 43 models were systematically identified and assessed. The existing models showed good discrimination, but lacked external validation. Deep learning algorithms were increasingly being used with good performance. More researches were required to improve the quality of deep learning models, particularly for the Asian population.